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The role of green and sustainable finance is crucial as the global economy, marked by increasing energy demands and environmental concerns, the need for creative solutions to meet humans need and create a better future for all, transitions towards a low-carbon, sustainable, and resilient future. This study examines the impact of green finance on the economic prosperity of the Sahel from 2001 – 2022. Descriptive statistics, boxplots, cointegration tests, and Vector Error Correction Model (VECM) analysis, were utilized to explore the interplay between the variables with the aid of EViews software. The results reveal the growth patterns of GDP, EGLC, POP, INFL, CO2 and FDI, and their significance in driving economic expansion in the Sahel. This study concludes with actionable insights and recommendations for Sahel policymakers to address energy poverty, energy transition, demographic trends, climate change, investments, financing, institutional quality, and other economic issues.

The present study examines whether the Russian economy exhibits the symptoms of the Dutch Disease over the transition period begun in the early 1990s. Five warning signs have been detected, namely, a real exchange rate appreciation (1); a flourishing economic situation pushed by higher oil prices (2); a relative de-industrialisation (3); an export reduction in the non-booming-sector (4) and a real wage growth (5). The first three symptoms are estimated simultaneously in a VECM dimension. The results suggest the existence of three long-run cointegrating vectors, thus confirming the presence of the first three symptoms. Specifically, a 10% oil price shock leads to a real appreciation by 4%, a rise in GDP by 3% and a decline in domestic manufacturing production vis-à-vis service production by another 3%. Finally, a number of manufacturing exports have been crowded out and real wages have recorded important increases. To a certain extent, this corroborates the presence of symptom 4 and 5. The paper concludes that the risk of the Dutch Disease exists, and two preventive thrusts of action could be undertaken to reduce its threat: namely to diversify the economy and to hold back the appreciation of the exchange rate through targeted fiscal and monetary policies. These instruments would render Russia less vulnerable to exogenous shocks.

Türkiye'de 1990-2017 yıllan arasında fosil ve yenilenebilir kaynaklı elektrik üretiminin, ekonomik büyüme ve çevresel kirlilikle olan ilişkilerinin incelenmesi makalenin amacını oluşturmaktadır. Deǧişkenler arası ilişkilerin tespiti için Johansen-Juselius (1990) eşbütünleşme testi ile VECM nedensellik analizinin aşamaları takip edilmiştir. Analiz sonuçlarına göre; i) Deǧişkenler arasında eşbütünleşme ilişkisi bulunmaktadır. ii) Uzun dönemde fosil ve yenilenebilir kaynaklı elektrik üretimleri açısından koruma hipotezinin geçerli olduǧu görülmektedir. iii) Karbondioksit salınımındaki artışlar yenilenebilir elektrik üretimini fosil kaynaklı elektrik üretiminden daha fazla arttırmaktadır. iv) Elektrik üretiminde kullanılan kaynakların ikame ilişkisine göre fosil kaynaklı elektrik üretimindeki artışlar yenilenebilir elektrik üretimini daha fazla azaltmaktadır. Sonuç olarak kaynaǧına göre elektrik üretimi üzerinde ekonomik büyümenin, çevresel tepkilerin ve kaynakların ikame derecesinin etkili olduǧu görülmektedir. Çevre kalitesinin saǧlanması açısından etkenlerin birbirleriyle olan etkileşimlerine dikkat etmek gerekmektedir.

Abstract Purpose: Investor sentiment in financial markets has a close relationship with the general mood prevailing in the environment such as economic, social and political life. Future economic expectations are important for both investors and policymakers. Investor sentiment and macroeconomic variables are likely to affect each other. Emerging countries are particularly sensitive to interest and foreign exchange risk. Turkey is an important emerging country. The effects of interest rate and exchange fluctuations are high in this country. The aim of this study is to reveal the relationship between investor sentiment and interest and foreign exchange rates in Turkey. Methodology: This study investigates the relationship between economic confidence index, exchange rates and interest rates in Turkey during the period between January 2012 and November 2019 using monthly data sets. The economic confidence index is used to represent the investor sentiment in the study. Interest rate variables are the deposit interest rates and the commercial credit interest rates. The representative of the US dollar currency variables is included in the analysis. This chapter used the time series vector error correction model approach of stationarity test, cointegration test and Granger causality test. Findings: According to the causality test, there is a two-way relationship between economic confidence index and exchange rate, and there is uni-directional causality from commercial credit interest rate to economic confidence index. The results show that foreign exchange and commercial credit interest rate variables are carefully monitored by market players and are effective and influential in the formation of future expectations. Originality/value: The study shows the direction of the relationship between economic confidence foreign exchange and commercial credit interest rate. Policymakers can shape expectations by taking into account the direction of the relationship.

This study examines the impact of economic growth, energy consumption, trade openness, financial development on carbon emissions for the case of Turkey by using annual time series data for the period of 1960–2013. The Lee and Strazicich test suggests that the variables are suitable for applying the bounds testing approach to cointegration. The cointegration analysis reveals that there exists a long-run relationship between the per capita real income, per capita energy consumption, trade openness, financial development, and per capita carbon emissions in the presence of structural breaks. The results show that in the long run, carbon emissions are mainly determined by economic growth, energy consumption, trade openness, and financial development. The VECM Granger causality analysis indicates a long-run unidirectional causality running from economic growth, energy consumption, trade openness, and financial development to carbon emissions. The findings also show that the EKC hypothesis is valid for Turkey both in the long run and short run. The study provides some implications for policy makers to decrease carbon emissions in Turkey.

The study revisits the position of the environmental Kuznets curve (EKC) hypothesis in India by incorporating the role of energy consumption and democratic regime in the environmental degradation function for the period 1971–2014. Employing Zivot–Andrews nonstationarity test, Bayer–Hanck cointegration test, autoregressive distributed lag (ARDL) model, and vector autoregressive model (VECM) Granger causality test, the results found the integration order of I(1) and a stable cointegration among the series. The result validates the EKC hypothesis for India and further divulges that while energy consumption increases environmental degradation both in the long run and short run; the effect of democracy in reducing environmental degradation is weak (statistically insignificant) in the long run but strong (statistically significant) in the short run. The finding from the VECM Granger causality test indicates a long-run causality between the fundamental variables and environmental degradation. Furthermore, the results of the short run show a unidirectional Granger causality running from energy consumption to environmental degradation, energy consumption to real income, and energy consumption to square of real income. Therefore, our findings suggest that energy conservation policy should be prioritized towards harnessing energy from clean sources to mitigate environmental degradation and spur economic growth.

Over the last few years, global warming and rapid climate change have become major risk factors that pose a serious threat to global security. A key factor behind these risk factors is greenhouse gases, which emit mainly carbon dioxide (CO 2 ). The existing literature seeks to determine the economic and non-economic aspects of CO 2 emissions to prevent environmental degradation. However, the effects of economic policy uncertainty and foreign direct investment on CO 2 emissions are undeniable. This study examines the impact of economic policy uncertainty and foreign direct investment on CO 2 emissions in the panel of 24 developed and developing nations from 2001 to 2019. After verifying cross-sectional dependency and co-integration among parameters, the dynamic seemingly unrelated regression and panel vector error correction model (VECM) Granger causality methods are used for long-run estimates and verify the causal link among variables. Our findings show that economic policy uncertainty, economic growth, trade, and energy consumption adversely impact the environment, while foreign direct investment enhances sample countries’ environmental quality. Furthermore, a bidirectional relationship exists between CO 2 , economic policy uncertainty, economic growth, trade, and energy consumption. In addition, this study observed similar results in a robustness analysis using the dynamic common correlated effects and fixed effect panel quantile regression frameworks. Based on the inclusive outcomes, this study forms significant suggestions for policy implications. Specifically, policymakers should design environmental-friendly trade policies, explore renewable energy options, and implement green investment and financing strategies to improve the environment. Graphical abstract

Purpose With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019. Design/methodology/approach The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework. Findings The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect. Originality/value Results here should be of use to policymakers in certain policy analysis.

In this paper, the relationship between carbon dioxide and agriculture in Ghana was investigated by comparing a Vector Error Correction Model (VECM) and Autoregressive Distributed Lag (ARDL) Model. Ten study variables spanning from 1961 to 2012 were employed from the Food Agricultural Organization. Results from the study show that carbon dioxide emissions affect the percentage annual change of agricultural area, coarse grain production, cocoa bean production, fruit production, vegetable production, and the total livestock per hectare of the agricultural area. The vector error correction model and the autoregressive distributed lag model show evidence of a causal relationship between carbon dioxide emissions and agriculture; however, the relationship decreases periodically which may die over-time. All the endogenous variables except total primary vegetable production lead to carbon dioxide emissions, which may be due to poor agricultural practices to meet the growing food demand in Ghana. The autoregressive distributed lag bounds test shows evidence of a long-run equilibrium relationship between the percentage annual change of agricultural area, cocoa bean production, total livestock per hectare of agricultural area, total pulses production, total primary vegetable production, and carbon dioxide emissions. It is important to end hunger and ensure people have access to safe and nutritious food, especially the poor, orphans, pregnant women, and children under-5 years in order to reduce maternal and infant mortalities. Nevertheless, it is also important that the Government of Ghana institutes agricultural policies that focus on promoting a sustainable agriculture using environmental friendly agricultural practices. The study recommends an integration of climate change measures into Ghana’s national strategies, policies and planning in order to strengthen the country’s effort to achieving a sustainable environment.

This study has examined the long-run relationships among system-level AI readiness, STEM human capital capacity, economic performance, and climate transition in China over the period of 1980–2024. Rather than evaluating the classroom-level learning outcomes, analysis conceptualizes the AI-driven personalized learning systems (AI-PLS) as a macro-level proxy for the national capacity to deploy AI-enabled education and innovation infrastructures. Multidimensional indices are constructed using the principal component analysis, and long-run and short-run dynamics are examined through the Johansen cointegration, vector error correction models, impulse response functions, forecast error variance decomposition, and wavelet coherence analysis. Results have indicated that AI readiness and STEM capacity are strongly and positively associated with the economic modernization over long run, reflecting complementarities between digital capability, human capital formation, and growth. At same time, AI readiness exhibits the negative long-run association with climate transition index, which is more plausibly interpreted as reflecting energy- and emissions-intensive nature of the economy-wide digitalization and industrial upgrading, rather than environmental effects of the educational technologies per se. Climate transition dynamics adjust more slowly than economic and STEM indicators, underscoring structural rigidities in the energy systems and longer time horizons required for decarbonization. Overall, findings suggest that while the AI-enabled education and innovation capacity can support the economic growth, sustainability benefits are conditional on the complementary energy, governance, and climate policies. Policy implications have highlighted importance of the aligning AI adoption strategies with the low-carbon development pathways, strengthening linkages between STEM education and the green innovation, and adopting the integrated governance frameworks that recognize the interdependencies across education, technology, economy, and environment. Study’s scope is limited by its focus on the single country, reliance on macro-level secondary data, and the linear modeling assumptions. Future research may has extended to this framework through the cross-country comparisons, nonlinear and structural-break analyses, and incorporation of the micro-level evidence.